This paper presents an intelligent sewing system for personalized stent graft manufacturing, a challenging sewing task that is currently performed manually. Inspired by medical suturing robots, we have adopted a single-sided sewing technique using a curved needle to perform the task of sewing stents onto fabric. A motorized surgical needle driver was attached to a 7 d.o.f robot arm to manipulate the needle with a second robot controlling the position of the mandrel. A learningfrom-demonstration approach was used to program the robot to sew stents onto fabric. The demonstrated sewing skill was segmented to several phases, each of which was encoded with a Gaussian Mixture Model. Generalized sewing movements were then generated from these models and were used for task execution. During execution, a stereo vision system was adopted to guide the robots to adjust the learnt movements according to the needle pose. Two experiments are presented here with this system and the results show that our system can robustly perform the sewing task as well as adapt to various needle poses. The accuracy of the sewing system was within 2mm.
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